机器视觉开源代码集合(转载)【88bf必发娱乐】

    
本文属于转载内容,也在笔者另外一个博客(http://blog.csdn.net/qq\_37608890/article/details/79352633)发布。

机器视觉开源代码集合(转载),视觉源代码

    
本文属于转载内容,也在笔者另外一个博客(http://blog.csdn.net/qq\_37608890/article/details/79352633)发布。

来源:http://blog.csdn.net/flyingpig851334799/article/details/47449847。

一、特征提取Feature Extraction:

  • SIFT [1] [Demo program][SIFT Library] [VLFeat]
  • PCA-SIFT [2] [Project]
  • Affine-SIFT [3] [Project]
  • SURF [4] [OpenSURF] [Matlab Wrapper]
  • Affine Covariant Features [5] [Oxford project]
  • MSER [6] [Oxford project] [VLFeat]
  • Geometric Blur [7] [Code]
  • Local Self-Similarity Descriptor [8] [Oxford implementation]
  • Global and Efficient Self-Similarity [9] [Code]
  • Histogram of Oriented Graidents [10] [INRIA Object Localization
    Toolkit] [OLT toolkit for Windows]
  • GIST [11] [Project]
  • Shape Context [12] [Project]
  • Color Descriptor [13] [Project]
  • Pyramids of Histograms of Oriented Gradients [Code]
  • Space-Time Interest Points (STIP) [14][Project] [Code]
  • Boundary Preserving Dense Local Regions [15][Project]
  • Weighted Histogram[Code]
  • Histogram-based Interest Points Detectors[Paper][Code]
  • An OpenCV – C++ implementation of Local Self Similarity Descriptors
    [Project]
  • Fast Sparse Representation with Prototypes[Project]
  • Corner Detection [Project]
  • AGAST Corner Detector: faster than FAST and even FAST-ER[Project]
  • Real-time Facial Feature Detection using Conditional Regression
    Forests[Project]
  • Global and Efficient Self-Similarity for Object Classification and
    Detection[code]
  • WαSH: Weighted α-Shapes for Local Feature Detection[Project]
  • HOG[Project]
  • Online Selection of Discriminative Tracking Features[Project]

二、图像分割Image Segmentation:

  • Normalized Cut [1] [Matlab code]
  • Gerg Mori’ Superpixel code [2] [Matlab code]
  • Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab
    wrapper]
  • Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab
    wrapper]
  • OWT-UCM Hierarchical Segmentation [5] [Resources]
  • Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit]
    [Updated code]
  • Quick-Shift [7] [VLFeat]
  • SLIC Superpixels [8] [Project]
  • Segmentation by Minimum Code Length [9] [Project]
  • Biased Normalized Cut [10] [Project]
  • Segmentation Tree [11-12] [Project]
  • Entropy Rate Superpixel Segmentation [13] [Code]
  • Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]
  • Efficient Planar Graph Cuts with Applications in Computer
    Vision[Paper][Code]
  • Isoperimetric Graph Partitioning for Image
    Segmentation[Paper][Code]
  • Random Walks for Image Segmentation[Paper][Code]
  • Blossom V: A new implementation of a minimum cost perfect matching
    algorithm[Code]
  • An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy
    Minimization in Computer Vision[Paper][Code]
  • Geodesic Star Convexity for Interactive Image
    Segmentation[Project]
  • Contour Detection and Image Segmentation
    Resources[Project][Code]
  • Biased Normalized Cuts[Project]
  • Max-flow/min-cut[Project]
  • Chan-Vese Segmentation using Level Set[Project]
  • A Toolbox of Level Set Methods[Project]
  • Re-initialization Free Level Set Evolution via Reaction
    Diffusion[Project]
  • Improved C-V active contour model[Paper][Code]
  • A Variational Multiphase Level Set Approach to Simultaneous
    Segmentation and Bias Correction[Paper][Code]
  • Level Set Method Research by Chunming Li[Project]
  • ClassCut for Unsupervised Class Segmentation[code]
  • SEEDS: Superpixels Extracted via Energy-Driven
    Sampling [Project][other]

三、目标检测Object Detection:

  • A simple object detector with boosting [Project]
  • INRIA Object Detection and Localization Toolkit [1] [Project]
  • Discriminatively Trained Deformable Part Models [2] [Project]
  • Cascade Object Detection with Deformable Part Models [3]
    [Project]
  • Poselet [4] [Project]
  • Implicit Shape Model [5] [Project]
  • Viola and Jones’s Face Detection [6] [Project]
  • Bayesian Modelling of Dyanmic Scenes for Object
    Detection[Paper][Code]
  • Hand detection using multiple proposals[Project]
  • Color Constancy, Intrinsic Images, and Shape
    Estimation[Paper][Code]
  • Discriminatively trained deformable part models[Project]
  • Gradient Response Maps for Real-Time Detection of Texture-Less
    Objects: LineMOD [Project]
  • Image Processing On Line[Project]
  • Robust Optical Flow Estimation[Project]
  • Where’s Waldo: Matching People in Images of Crowds[Project]
  • Scalable Multi-class Object Detection[Project]
  • Class-Specific Hough Forests for Object Detection[Project]
  • Deformed Lattice Detection In Real-World Images[Project]
  • Discriminatively trained deformable part models[Project]

四、显著性检测Saliency Detection:

  • Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]
  • Frequency-tuned salient region detection [2] [Project]
  • Saliency detection using maximum symmetric surround [3]
    [Project]
  • Attention via Information Maximization [4] [Matlab code]
  • Context-aware saliency detection [5] [Matlab code]
  • Graph-based visual saliency [6] [Matlab code]
  • Saliency detection: A spectral residual approach. [7] [Matlab
    code]
  • Segmenting salient objects from images and videos. [8] [Matlab
    code]
  • Saliency Using Natural statistics. [9] [Matlab code]
  • Discriminant Saliency for Visual Recognition from Cluttered Scenes.
    [10] [Code]
  • Learning to Predict Where Humans Look [11] [Project]
  • Global Contrast based Salient Region Detection [12] [Project]
  • Bayesian Saliency via Low and Mid Level Cues[Project]
  • Top-Down Visual Saliency via Joint CRF and Dictionary
    Learning[Paper][Code]
  • Saliency Detection: A Spectral Residual Approach[Code]

五、图像分类、聚类Image Classification, Clustering

  • Pyramid Match [1] [Project]
  • Spatial Pyramid Matching [2] [Code]
  • Locality-constrained Linear Coding [3] [Project] [Matlab code]
  • Sparse Coding [4] [Project] [Matlab code]
  • Texture Classification [5] [Project]
  • Multiple Kernels for Image Classification [6] [Project]
  • Feature Combination [7] [Project]
  • SuperParsing [Code]
  • Large Scale Correlation Clustering Optimization[Matlab code]
  • Detecting and Sketching the Common[Project]
  • Self-Tuning Spectral Clustering[Project][Code]
  • User Assisted Separation of Reflections from a Single Image Using a
    Sparsity Prior[Paper][Code]
  • Filters for Texture Classification[Project]
  • Multiple Kernel Learning for Image Classification[Project]
  • SLIC Superpixels[Project]

六、抠图Image Matting

  • A Closed Form Solution to Natural Image Matting [Code]
  • Spectral Matting [Project]
  • Learning-based Matting [Code]

七、目标跟踪Object Tracking:

  • A Forest of Sensors – Tracking Adaptive Background Mixture Models
    [Project]
  • Object Tracking via Partial Least Squares Analysis[Paper][Code]
  • Robust Object Tracking with Online Multiple Instance
    Learning[Paper][Code]
  • Online Visual Tracking with Histograms and Articulating
    Blocks[Project]
  • Incremental Learning for Robust Visual Tracking[Project]
  • Real-time Compressive Tracking[Project]
  • Robust Object Tracking via Sparsity-based Collaborative
    Model[Project]
  • Visual Tracking via Adaptive Structural Local Sparse Appearance
    Model[Project]
  • Online Discriminative Object Tracking with Local Sparse
    Representation[Paper][Code]
  • Superpixel Tracking[Project]
  • Learning Hierarchical Image Representation with Sparsity, Saliency
    and Locality[Paper][Code]
  • Online Multiple Support Instance Tracking [Paper][Code]
  • Visual Tracking with Online Multiple Instance Learning[Project]
  • Object detection and recognition[Project]
  • Compressive Sensing Resources[Project]
  • Robust Real-Time Visual Tracking using Pixel-Wise
    Posteriors[Project]
  • Tracking-Learning-Detection[Project][OpenTLD/C++ Code]
  • the HandVu:vision-based hand gesture interface[Project]
  • Learning Probabilistic Non-Linear Latent Variable Models for
    Tracking Complex Activities[Project]

八、Kinect:

  • Kinect toolbox[Project]
  • OpenNI[Project]
  • zouxy09 CSDN Blog[Resource]
  • FingerTracker 手指跟踪[code]

九、3D相关:

  • 3D Reconstruction of a Moving Object[Paper] [Code]
  • Shape From Shading Using Linear Approximation[Code]
  • Combining Shape from Shading and Stereo Depth
    Maps[Project][Code]
  • Shape from Shading: A Survey[Paper][Code]
  • A Spatio-Temporal Descriptor based on 3D Gradients
    (HOG3D)[Project][Code]
  • Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]
  • A Fast Marching Formulation of Perspective Shape from Shading under
    Frontal Illumination[Paper][Code]
  • Reconstruction:3D Shape, Illumination, Shading, Reflectance,
    Texture[Project]
  • Monocular Tracking of 3D Human Motion with a Coordinated Mixture of
    Factor Analyzers[Code]
  • Learning 3-D Scene Structure from a Single Still Image[Project]

十、机器学习算法:

  • Matlab class for computing Approximate Nearest Nieghbor (ANN)
    [Matlab class providing interface toANN library]
  • Random Sampling[code]
  • Probabilistic Latent Semantic Analysis (pLSA)[Code]
  • FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]
  • Fast Intersection / Additive Kernel SVMs[Project]
  • SVM[Code]
  • Ensemble learning[Project]
  • Deep Learning[Net]
  • Deep Learning Methods for Vision[Project]
  • Neural Network for Recognition of Handwritten Digits[Project]
  • Training a deep autoencoder or a classifier on MNIST
    digits[Project]
  • THE MNIST DATABASE of handwritten digits[Project]
  • Ersatz:deep neural networks in the cloud[Project]
  • Deep Learning [Project]
  • sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in
    C/C++[Project]
  • Weka 3: Data Mining Software in Java[Project]
  • Invited talk “A Tutorial on Deep Learning” by Dr. Kai Yu
    (余凯)[Video]
  • CNN – Convolutional neural network class[Matlab Tool]
  • Yann LeCun’s Publications[Wedsite]
  • LeNet-5, convolutional neural networks[Project]
  • Training a deep autoencoder or a classifier on MNIST
    digits[Project]
  • Deep Learning 大牛Geoffrey E. Hinton’s HomePage[Website]
  • Multiple Instance Logistic Discriminant-based Metric Learning
    (MildML) and Logistic Discriminant-based Metric Learning
    (LDML)[Code]
  • Sparse coding simulation software[Project]
  • Visual Recognition and Machine Learning Summer School[Software]

十一、目标、行为识别Object, Action Recognition:

  • Action Recognition by Dense Trajectories[Project][Code]
  • Action Recognition Using a Distributed Representation of Pose and
    Appearance[Project]
  • Recognition Using Regions[Paper][Code]
  • 2D Articulated Human Pose Estimation[Project]
  • Fast Human Pose Estimation Using Appearance and Motion via
    Multi-Dimensional Boosting Regression[Paper][Code]
  • Estimating Human Pose from Occluded Images[Paper][Code]
  • Quasi-dense wide baseline matching[Project]
  • ChaLearn Gesture Challenge: Principal motion: PCA-based
    reconstruction of motion histograms[Project]
  • Real Time Head Pose Estimation with Random Regression
    Forests[Project]
  • 2D Action Recognition Serves 3D Human Pose Estimation[Project]
  • A Hough Transform-Based Voting Framework for Action
    Recognition[Project]
  • Motion Interchange Patterns for Action Recognition in Unconstrained
    Videos[Project]
  • 2D articulated human pose estimation software[Project]
  • Learning and detecting shape models [code]
  • Progressive Search Space Reduction for Human Pose
    Estimation[Project]
  • Learning Non-Rigid 3D Shape from 2D Motion[Project]

十二、图像处理:

  • Distance Transforms of Sampled Functions[Project]
  • The Computer Vision Homepage[Project]
  • Efficient appearance distances between windows[code]
  • Image Exploration algorithm[code]
  • Motion Magnification 运动放大 [Project]
  • Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]
  • A Fast Approximation of the Bilateral Filter using a Signal
    Processing Approach [Project]

十三、一些实用工具:

  • EGT: a Toolbox for Multiple View Geometry and Visual
    Servoing[Project] [Code]
  • a development kit of matlab mex functions for OpenCV
    library[Project]
  • Fast Artificial Neural Network Library[Project]

十四、人手及指尖检测与识别:

  • finger-detection-and-gesture-recognition [Code]
  • Hand and Finger Detection using JavaCV[Project]
  • Hand and fingers detection[Code]

十五、场景解释:

  • Nonparametric Scene Parsing via Label Transfer [Project]

十六、光流Optical flow:

  • High accuracy optical flow using a theory for warping [Project]
  • Dense Trajectories Video Description [Project]
  • SIFT Flow: Dense Correspondence across Scenes and its
    Applications[Project]
  • KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker
    [Project]
  • Tracking Cars Using Optical Flow[Project]
  • Secrets of optical flow estimation and their principles[Project]
  • implmentation of the Black and Anandan dense optical flow
    method[Project]
  • Optical Flow Computation[Project]
  • Beyond Pixels: Exploring New Representations and Applications for
    Motion Analysis[Project]
  • A Database and Evaluation Methodology for Optical Flow[Project]
  • optical flow relative[Project]
  • Robust Optical Flow Estimation [Project]
  • optical flow[Project]

十七、图像检索Image Retrieval:

  • Semi-Supervised Distance Metric Learning for Collaborative Image
    Retrieval [Paper][code]

十八、马尔科夫随机场Markov Random Fields:

  • Markov Random Fields for Super-Resolution [Project]
  • A Comparative Study of Energy Minimization Methods for Markov Random
    Fields with Smoothness-Based Priors [Project]

十九、运动检测Motion detection:

  • Moving Object Extraction, Using Models or Analysis of
    Regions [Project]
  • Background Subtraction: Experiments and Improvements for ViBe
    [Project]
  • A Self-Organizing Approach to Background Subtraction for Visual
    Surveillance Applications [Project]
  • changedetection.net: A new change detection benchmark
    dataset[Project]
  • ViBe – a powerful technique for background detection and subtraction
    in video sequences[Project]
  • Background Subtraction Program[Project]
  • Motion Detection Algorithms[Project]
  • Stuttgart Artificial Background Subtraction Dataset[Project]
  • Object Detection, Motion Estimation, and Tracking[Project]

Feature Detection and Description

General Libraries: 

  • VLFeat – Implementation of various feature descriptors (including
    SIFT, HOG, and LBP) and covariant feature detectors (including DoG,
    Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian,
    Multiscale Harris). Easy-to-use Matlab interface. See Modern
    features: Software – Slides providing a demonstration of VLFeat and
    also links to other software. Check also VLFeat hands-on session
    training
  • OpenCV – Various implementations of modern feature detectors and
    descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)

Fast Keypoint Detectors for Real-time Applications: 

  • FAST – High-speed corner detector implementation for a wide variety
    of platforms
  • AGAST – Even faster than the FAST corner detector. A multi-scale
    version of this method is used for the BRISK descriptor (ECCV 2010).

Binary Descriptors for Real-Time Applications: 

  • BRIEF – C++ code for a fast and accurate interest point descriptor
    (not invariant to rotations and scale) (ECCV 2010)
  • ORB – OpenCV implementation of the Oriented-Brief (ORB) descriptor
    (invariant to rotations, but not scale)
  • BRISK – Efficient Binary descriptor invariant to rotations and
    scale. It includes a Matlab mex interface. (ICCV 2011)
  • FREAK – Faster than BRISK (invariant to rotations and scale)
    (CVPR 2012)

SIFT and SURF Implementations: 

  • SIFT: VLFeat, OpenCV, Original code by David Lowe, GPU
    implementation, OpenSIFT
  • SURF: Herbert Bay’s code, OpenCV, GPU-SURF

Other Local Feature Detectors and Descriptors: 

  • VGG Affine Covariant features – Oxford code for various affine
    covariant feature detectors and descriptors.
  • LIOP descriptor – Source code for the Local Intensity order Pattern
    (LIOP) descriptor (ICCV 2011).
  • Local Symmetry Features – Source code for matching of local symmetry
    features under large variations in lighting, age, and rendering
    style (CVPR 2012).

Global Image Descriptors: 

  • GIST – Matlab code for the GIST descriptor
  • CENTRIST – Global visual descriptor for scene categorization and
    object detection (PAMI 2011)

Feature Coding and Pooling 

  • VGG Feature Encoding Toolkit – Source code for various
    state-of-the-art feature encoding methods – including Standard hard
    encoding, Kernel codebook encoding, Locality-constrained linear
    encoding, and Fisher kernel encoding.
  • Spatial Pyramid Matching – Source code for feature pooling based on
    spatial pyramid matching (widely used for image classification)

Convolutional Nets and Deep Learning 

  • EBLearn – C++ Library for Energy-Based Learning. It includes several
    demos and step-by-step instructions to train classifiers based on
    convolutional neural networks.
  • Torch7 – Provides a matlab-like environment for state-of-the-art
    machine learning algorithms, including a fast implementation of
    convolutional neural networks.
  • Deep Learning – Various links for deep learning software.

Part-Based Models 

  • Deformable Part-based Detector – Library provided by the authors of
    the original paper (state-of-the-art in PASCAL VOC detection task)
  • Efficient Deformable Part-Based Detector – Branch-and-Bound
    implementation for a deformable part-based detector.
  • Accelerated Deformable Part Model – Efficient implementation of a
    method that achieves the exact same performance of deformable
    part-based detectors but with significant acceleration (ECCV 2012).
  • Coarse-to-Fine Deformable Part Model – Fast approach for deformable
    object detection (CVPR 2011).
  • Poselets – C++ and Matlab versions for object detection based on
    poselets.
  • Part-based Face Detector and Pose Estimation – Implementation of a
    unified approach for face detection, pose estimation, and landmark
    localization (CVPR 2012).

Attributes and Semantic Features 

  • Relative Attributes – Modified implementation of RankSVM to train
    Relative Attributes (ICCV 2011).
  • Object Bank – Implementation of object bank semantic features (NIPS
    2010). See also ActionBank
  • Classemes, Picodes, and Meta-class features – Software for
    extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR
    2012).

Large-Scale Learning 

  • Additive Kernels – Source code for fast additive kernel SVM
    classifiers (PAMI 2013).
  • LIBLINEAR – Library for large-scale linear SVM classification.
  • VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.

Fast Indexing and Image Retrieval 

  • FLANN – Library for performing fast approximate nearest neighbor.
  • Kernelized LSH – Source code for Kernelized Locality-Sensitive
    Hashing (ICCV 2009).
  • ITQ Binary codes – Code for generation of small binary codes using
    Iterative Quantization and other baselines such as
    Locality-Sensitive-Hashing (CVPR 2011).
  • INRIA Image Retrieval – Efficient code for state-of-the-art
    large-scale image retrieval (CVPR 2011).

Object Detection 

  • See Part-based Models and Convolutional Nets above.
  • Pedestrian Detection at 100fps – Very fast and accurate pedestrian
    detector (CVPR 2012).
  • Caltech Pedestrian Detection Benchmark – Excellent resource for
    pedestrian detection, with various links for state-of-the-art
    implementations.
  • OpenCV – Enhanced implementation of Viola&Jones real-time object
    detector, with trained models for face detection.
  • Efficient Subwindow Search – Source code for branch-and-bound
    optimization for efficient object localization (CVPR 2008).

3D Recognition 

  • Point-Cloud Library – Library for 3D image and point cloud
    processing.

Action Recognition 

  • ActionBank – Source code for action recognition based on the
    ActionBank representation (CVPR 2012).
  • STIP Features – software for computing space-time interest point
    descriptors
  • Independent Subspace Analysis – Look for Stacked ISA for Videos
    (CVPR 2011)
  • Velocity Histories of Tracked Keypoints – C++ code for activity
    recognition using the velocity histories of tracked keypoints
    (ICCV 2009)

Datasets

Attributes 

  • Animals with Attributes – 30,475 images of 50 animals classes with 6
    pre-extracted feature representations for each image.
  • aYahoo and aPascal – Attribute annotations for images collected from
    Yahoo and Pascal VOC 2008.
  • FaceTracer – 15,000 faces annotated with 10 attributes and fiducial
    points.
  • PubFig – 58,797 face images of 200 people with 73 attribute
    classifier outputs.
  • LFW – 13,233 face images of 5,749 people with 73 attribute
    classifier outputs.
  • Human Attributes – 8,000 people with annotated attributes. Check
    also this link for another dataset of human attributes.
  • SUN Attribute Database – Large-scale scene attribute database with a
    taxonomy of 102 attributes.
  • ImageNet Attributes – Variety of attribute labels for the ImageNet
    dataset.
  • Relative attributes – Data for OSR and a subset of PubFig datasets.
    Check also this link for the WhittleSearch data.
  • Attribute Discovery Dataset – Images of shopping categories
    associated with textual descriptions.

Fine-grained Visual Categorization 

  • Caltech-UCSD Birds Dataset – Hundreds of bird categories with
    annotated parts and attributes.
  • Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from
    around the world.
  • Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200
    images for each class. Pixel level trimap segmentation is included.
  • Leeds Butterfly Dataset – 832 images of 10 species of butterflies.
  • Oxford Flower Dataset – Hundreds of flower categories.

Face Detection 

  • FDDB – UMass face detection dataset and benchmark (5,000+ faces)
  • CMU/MIT – Classical face detection dataset.

Face Recognition 

  • Face Recognition Homepage – Large collection of face recognition
    datasets.
  • LFW – UMass unconstrained face recognition dataset (13,000+ face
    images).
  • NIST Face Homepage – includes face recognition grand challenge
    (FRGC), vendor tests (FRVT) and others.
  • CMU Multi-PIE – contains more than 750,000 images of 337 people,
    with 15 different views and 19 lighting conditions.
  • FERET – Classical face recognition dataset.
  • Deng Cai’s face dataset in Matlab Format – Easy to use if you want
    play with simple face datasets including Yale, ORL, PIE, and
    Extended Yale B.
  • SCFace – Low-resolution face dataset captured from surveillance
    cameras.

Handwritten Digits 

  • MNIST – large dataset containing a training set of 60,000 examples,
    and a test set of 10,000 examples.

Pedestrian Detection

  • Caltech Pedestrian Detection Benchmark – 10 hours of video taken
    from a vehicle,350K bounding boxes for about 2.3K unique
    pedestrians.
  • INRIA Person Dataset – Currently one of the most popular pedestrian
    detection datasets.
  • ETH Pedestrian Dataset – Urban dataset captured from a stereo rig
    mounted on a stroller.
  • TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded
    in an crowded urban setting with an onboard camera.
  • PASCAL Human Detection – One of 20 categories in PASCAL VOC
    detection challenges.
  • USC Pedestrian Dataset – Small dataset captured from surveillance
    cameras.

Generic Object Recognition 

  • ImageNet – Currently the largest visual recognition dataset in terms
    of number of categories and images.
  • Tiny Images – 80 million 32×32 low resolution images.
  • Pascal VOC – One of the most influential visual recognition
    datasets.
  • Caltech 101 / Caltech 256 – Popular image datasets containing 101
    and 256 object categories, respectively.
  • MIT LabelMe – Online annotation tool for building computer vision
    databases.

Scene Recognition

  • MIT SUN Dataset – MIT scene understanding dataset.
  • UIUC Fifteen Scene Categories – Dataset of 15 natural scene
    categories.

Feature Detection and Description 

  • VGG Affine Dataset – Widely used dataset for measuring performance
    of feature detection and description. CheckVLBenchmarks for an
    evaluation framework.

Action Recognition

  • Benchmarking Activity Recognition – CVPR 2012 tutorial covering
    various datasets for action recognition.

RGBD Recognition 

  • RGB-D Object Dataset – Dataset containing 300 common household
    objects

Reference:

[1]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html

http://www.bkjia.com/Pythonjc/1305738.htmlwww.bkjia.comtruehttp://www.bkjia.com/Pythonjc/1305738.htmlTechArticle机器视觉开源代码集合(转载),视觉源代码
本文属于转载内容,也在笔者另外一个博客(http://blog.csdn.net/qq\_37608890/article/details/79352633)发

来源:http://blog.csdn.net/flyingpig851334799/article/details/47449847。

一、特征提取Feature
Extraction:

二、图像分割Image
Segmentation:

三、目标检测Object
Detection:

  • A simple
    object detector with boosting
    [Project]
  • INRIA
    Object Detection and Localization Toolkit [1]
    [Project]
  • Discriminatively
    Trained Deformable Part Models [2]
    [Project]
  • Cascade
    Object Detection with Deformable Part Models [3]
    [Project]
  • Poselet
    [4]
    [Project]
  • Implicit
    Shape Model [5]
    [Project]
  • Viola
    and Jones’s Face Detection [6]
    [Project]
  • Bayesian
    Modelling of Dyanmic Scenes for Object
    Detection[Paper][Code]
  • Hand
    detection using multiple
    proposals[Project]
  • Color
    Constancy, Intrinsic Images, and Shape
    Estimation[Paper][Code]
  • Discriminatively
    trained deformable part
    models[Project]
  • Gradient
    Response Maps for Real-Time Detection of Texture-Less Objects:
    LineMOD
    [Project]
  • Image
    Processing On Line[Project]
  • Robust
    Optical Flow
    Estimation[Project]
  • Where’s
    Waldo: Matching People in Images of
    Crowds[Project]
  • Scalable
    Multi-class Object
    Detection[Project]
  • Class-Specific
    Hough Forests for Object
    Detection[Project]
  • Deformed
    Lattice Detection In Real-World
    Images[Project]
  • Discriminatively
    trained deformable part
    models[Project]

四、显著性检测Saliency
Detection:

  • Itti,
    Koch, and Niebur’ saliency detection [1] [Matlab
    code
    ]
  • Frequency-tuned
    salient region detection [2]
    [Project]
  • Saliency
    detection using maximum symmetric surround [3]
    [Project]
  • Attention via
    Information Maximization [4] [Matlab
    code
    ]
  • Context-aware
    saliency detection [5] [Matlab
    code
    ]
  • Graph-based
    visual saliency [6] [Matlab
    code
    ]
  • Saliency
    detection: A spectral residual approach. [7] [Matlab
    code
    ]
  • Segmenting
    salient objects from images and videos. [8] [Matlab
    code
    ]
  • Saliency
    Using Natural statistics. [9] [Matlab
    code
    ]
  • Discriminant
    Saliency for Visual Recognition from Cluttered Scenes. [10]
    [Code]
  • Learning
    to Predict Where Humans Look [11]
    [Project]
  • Global
    Contrast based Salient Region Detection [12]
    [Project]
  • Bayesian
    Saliency via Low and Mid Level
    Cues[Project]
  • Top-Down
    Visual Saliency via Joint CRF and Dictionary
    Learning[Paper][Code]
  • Saliency
    Detection: A Spectral Residual
    Approach[Code]

五、图像分类、聚类Image
Classification, Clustering

  • Pyramid
    Match [1]
    [Project]
  • Spatial
    Pyramid Matching [2]
    [Code]
  • Locality-constrained
    Linear Coding [3]
    [Project]
    [Matlab
    code
    ]
  • Sparse
    Coding [4]
    [Project]
    [Matlab
    code
    ]
  • Texture
    Classification [5]
    [Project]
  • Multiple
    Kernels for Image Classification [6]
    [Project]
  • Feature
    Combination [7]
    [Project]
  • SuperParsing
    [Code]
  • Large
    Scale Correlation Clustering Optimization[Matlab
    code
    ]
  • Detecting and
    Sketching the
    Common[Project]
  • Self-Tuning
    Spectral
    Clustering[Project][Code]
  • User
    Assisted Separation of Reflections from a Single Image Using a
    Sparsity
    Prior[Paper][Code]
  • Filters
    for Texture
    Classification[Project]
  • Multiple
    Kernel Learning for Image
    Classification[Project]
  • SLIC
    Superpixels[Project]

六、抠图Image
Matting

  • A Closed
    Form Solution to Natural Image Matting
    [Code]
  • Spectral
    Matting
    [Project]
  • Learning-based
    Matting
    [Code]

七、目标跟踪Object
Tracking:

  • A Forest
    of Sensors – Tracking Adaptive Background Mixture Models
    [Project]
  • Object
    Tracking via Partial Least Squares
    Analysis[Paper][Code]
  • Robust
    Object Tracking with Online Multiple Instance
    Learning[Paper][Code]
  • Online
    Visual Tracking with Histograms and Articulating
    Blocks[Project]
  • Incremental
    Learning for Robust Visual
    Tracking[Project]
  • Real-time
    Compressive
    Tracking[Project]
  • Robust
    Object Tracking via Sparsity-based Collaborative
    Model[Project]
  • Visual
    Tracking via Adaptive Structural Local Sparse Appearance
    Model[Project]
  • Online
    Discriminative Object Tracking with Local Sparse
    Representation[Paper][Code]
  • Superpixel
    Tracking[Project]
  • Learning
    Hierarchical Image Representation with Sparsity, Saliency and
    Locality[Paper][Code]
  • Online
    Multiple Support Instance Tracking
    [Paper][Code]
  • Visual
    Tracking with Online Multiple Instance
    Learning[Project]
  • Object
    detection and
    recognition[Project]
  • Compressive
    Sensing Resources[Project]
  • Robust
    Real-Time Visual Tracking using Pixel-Wise
    Posteriors[Project]
  • Tracking-Learning-Detection[Project][OpenTLD/C++
    Code
    ]
  • the
    HandVu:vision-based hand gesture
    interface[Project]
  • Learning
    Probabilistic Non-Linear Latent Variable Models for Tracking Complex
    Activities[Project]

八、Kinect:

九、3D相关:

  • 3D
    Reconstruction of a Moving
    Object[Paper]
    [Code]
  • Shape
    From Shading Using Linear
    Approximation[Code]
  • Combining
    Shape from Shading and Stereo Depth
    Maps[Project][Code]
  • Shape
    from Shading: A
    Survey[Paper][Code]
  • A
    Spatio-Temporal Descriptor based on 3D Gradients
    (HOG3D)[Project][Code]
  • Multi-camera
    Scene Reconstruction via Graph
    Cuts[Paper][Code]
  • A Fast
    Marching Formulation of Perspective Shape from Shading under Frontal
    Illumination[Paper][Code]
  • Reconstruction:3D
    Shape, Illumination, Shading, Reflectance,
    Texture[Project]
  • Monocular
    Tracking of 3D Human Motion with a Coordinated Mixture of Factor
    Analyzers[Code]
  • Learning
    3-D Scene Structure from a Single Still
    Image[Project]

十、机器学习算法:

  • Matlab
    class for computing Approximate Nearest Nieghbor (ANN) [Matlab
    class
     providing
    interface toANN
    library
    ]
  • Random
    Sampling[code]
  • Probabilistic
    Latent Semantic Analysis
    (pLSA)[Code]
  • FASTANN
    and FASTCLUSTER for approximate k-means
    (AKM)[Project]
  • Fast
    Intersection / Additive Kernel
    SVMs[Project]
  • SVM[Code]
  • Ensemble
    learning[Project]
  • Deep
    Learning[Net]
  • Deep
    Learning Methods for
    Vision[Project]
  • Neural
    Network for Recognition of Handwritten
    Digits[Project]
  • Training
    a deep autoencoder or a classifier on MNIST
    digits[Project]
  • THE
    MNIST DATABASE of handwritten
    digits[Project]
  • Ersatz:deep
    neural networks in the
    cloud[Project]
  • Deep
    Learning
    [Project]
  • sparseLM
    : Sparse Levenberg-Marquardt nonlinear least squares in
    C/C++[Project]
  • Weka 3:
    Data Mining Software in
    Java[Project]
  • Invited
    talk “A Tutorial on Deep Learning” by Dr. Kai Yu
    (余凯)[Video]
  • CNN –
    Convolutional neural network class[Matlab
    Tool
    ]
  • Yann
    LeCun’s
    Publications[Wedsite]
  • LeNet-5,
    convolutional neural
    networks[Project]
  • Training
    a deep autoencoder or a classifier on MNIST
    digits[Project]
  • Deep
    Learning 大牛Geoffrey E. Hinton’s
    HomePage[Website]
  • Multiple
    Instance Logistic Discriminant-based Metric Learning (MildML) and
    Logistic Discriminant-based Metric Learning
    (LDML)[Code]
  • Sparse
    coding simulation
    software[Project]
  • Visual
    Recognition and Machine Learning Summer
    School[Software]

十一、目标、行为识别Object,
Action Recognition:

  • Action
    Recognition by Dense
    Trajectories[Project][Code]
  • Action
    Recognition Using a Distributed Representation of Pose and
    Appearance[Project]
  • Recognition
    Using
    Regions[Paper][Code]
  • 2D
    Articulated Human Pose
    Estimation[Project]
  • Fast
    Human Pose Estimation Using Appearance and Motion via
    Multi-Dimensional Boosting
    Regression[Paper][Code]
  • Estimating
    Human Pose from Occluded
    Images[Paper][Code]
  • Quasi-dense
    wide baseline
    matching[Project]
  • ChaLearn
    Gesture Challenge: Principal motion: PCA-based reconstruction of
    motion
    histograms[Project]
  • Real
    Time Head Pose Estimation with Random Regression
    Forests[Project]
  • 2D
    Action Recognition Serves 3D Human Pose
    Estimation[Project]
  • A Hough
    Transform-Based Voting Framework for Action
    Recognition[Project]
  • Motion
    Interchange Patterns for Action Recognition in Unconstrained
    Videos[Project]
  • 2D
    articulated human pose estimation
    software[Project]
  • Learning
    and detecting shape models
    [code]
  • Progressive
    Search Space Reduction for Human Pose
    Estimation[Project]
  • Learning
    Non-Rigid 3D Shape from 2D
    Motion[Project]

十二、图像处理:

  • Distance
    Transforms of Sampled
    Functions[Project]
  • The
    Computer Vision
    Homepage[Project]
  • Efficient
    appearance distances between
    windows[code]
  • Image
    Exploration
    algorithm[code]
  • Motion
    Magnification 运动放大
    [Project]
  • Bilateral
    Filtering for Gray and Color Images 双边滤波器
    [Project]
  • A Fast
    Approximation of the Bilateral Filter using a Signal Processing
    Approach
    [Project]

十三、一些实用工具:

  • EGT: a
    Toolbox for Multiple View Geometry and Visual
    Servoing[Project]
    [Code]
  • a
    development kit of matlab mex functions for OpenCV
    library[Project]
  • Fast
    Artificial Neural Network
    Library[Project]

十四、人手及指尖检测与识别:

  • finger-detection-and-gesture-recognition [Code]
  • Hand and
    Finger Detection using
    JavaCV[Project]
  • Hand and
    fingers
    detection[Code]

十五、场景解释:

  • Nonparametric
    Scene Parsing via Label
    Transfer [Project]

十六、光流Optical
flow:

  • High
    accuracy optical flow using a theory for
    warping [Project]
  • Dense
    Trajectories Video
    Description [Project]
  • SIFT
    Flow: Dense Correspondence across Scenes and its
    Applications[Project]
  • KLT: An
    Implementation of the Kanade-Lucas-Tomasi Feature Tracker
    [Project]
  • Tracking
    Cars Using Optical
    Flow[Project]
  • Secrets
    of optical flow estimation and their
    principles[Project]
  • implmentation
    of the Black and Anandan dense optical flow
    method[Project]
  • Optical
    Flow
    Computation[Project]
  • Beyond
    Pixels: Exploring New Representations and Applications for Motion
    Analysis[Project]
  • A
    Database and Evaluation Methodology for Optical
    Flow[Project]
  • optical
    flow
    relative[Project]
  • Robust
    Optical Flow Estimation
    [Project]
  • optical
    flow[Project]

十七、图像检索Image
Retrieval:

十八、马尔科夫随机场Markov
Random Fields:

十九、运动检测Motion
detection:

Feature
Detection and Description

General
Libraries: 

  • VLFeat –
    Implementation of various feature descriptors (including SIFT, HOG,
    and LBP) and covariant feature detectors (including DoG, Hessian,
    Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale
    Harris). Easy-to-use Matlab interface. See Modern features:
    Software
     –
    Slides providing a demonstration of VLFeat and also links to other
    software. Check also VLFeat hands-on session
    training
  • OpenCV –
    Various implementations of modern feature detectors and descriptors
    (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)

Fast
Keypoint Detectors for Real-time Applications: 

  • FAST –
    High-speed corner detector implementation for a wide variety of
    platforms
  • AGAST –
    Even faster than the FAST corner detector. A multi-scale version of
    this method is used for the BRISK descriptor (ECCV 2010).

Binary
Descriptors for Real-Time Applications: 

  • BRIEF –
    C++ code for a fast and accurate interest point descriptor (not
    invariant to rotations and scale) (ECCV 2010)
  • ORB –
    OpenCV implementation of the Oriented-Brief (ORB) descriptor
    (invariant to rotations, but not scale)
  • BRISK –
    Efficient Binary descriptor invariant to rotations and scale. It
    includes a Matlab mex interface. (ICCV 2011)
  • FREAK –
    Faster than BRISK (invariant to rotations and scale)
    (CVPR 2012)

SIFT and
SURF Implementations: 

Other Local
Feature Detectors and Descriptors: 

  • VGG
    Affine Covariant
    features
     – Oxford
    code for various affine covariant feature detectors and
    descriptors.
  • LIOP
    descriptor
     –
    Source code for the Local Intensity order Pattern (LIOP) descriptor
    (ICCV 2011).
  • Local
    Symmetry Features
     –
    Source code for matching of local symmetry features under large
    variations in lighting, age, and rendering style (CVPR 2012).

Global Image
Descriptors: 

  • GIST –
    Matlab code for the GIST descriptor
  • CENTRIST –
    Global visual descriptor for scene categorization and object
    detection (PAMI 2011)

Feature
Coding and Pooling 

  • VGG
    Feature Encoding
    Toolkit
     –
    Source code for various state-of-the-art feature encoding methods –
    including Standard hard encoding, Kernel codebook encoding,
    Locality-constrained linear encoding, and Fisher kernel
    encoding.
  • Spatial
    Pyramid Matching
     –
    Source code for feature pooling based on spatial pyramid matching
    (widely used for image classification)

Convolutional Nets
and Deep Learning 

  • EBLearn –
    C++ Library for Energy-Based Learning. It includes several demos and
    step-by-step instructions to train classifiers based on
    convolutional neural networks.
  • Torch7 –
    Provides a matlab-like environment for state-of-the-art machine
    learning algorithms, including a fast implementation of
    convolutional neural networks.
  • Deep
    Learning
     – Various links
    for deep learning software.

Part-Based
Models 

Attributes
and Semantic Features 

Large-Scale
Learning 

  • Additive
    Kernels
     – Source
    code for fast additive kernel SVM classifiers (PAMI 2013).
  • LIBLINEAR –
    Library for large-scale linear SVM classification.
  • VLFeat –
    Implementation for Pegasos SVM and Homogeneous Kernel map.

Fast
Indexing and Image Retrieval 

  • FLANN –
    Library for performing fast approximate nearest neighbor.
  • Kernelized
    LSH
     – Source
    code for Kernelized Locality-Sensitive Hashing (ICCV 2009).
  • ITQ
    Binary codes
     – Code for
    generation of small binary codes using Iterative Quantization and
    other baselines such as Locality-Sensitive-Hashing (CVPR
    2011).
  • INRIA
    Image Retrieval
     –
    Efficient code for state-of-the-art large-scale image retrieval
    (CVPR 2011).

Object
Detection 

3D
Recognition 

Action
Recognition 

Datasets

Attributes 

  • Animals
    with Attributes
     – 30,475
    images of 50 animals classes with 6 pre-extracted feature
    representations for each image.
  • aYahoo
    and aPascal
     – Attribute
    annotations for images collected from Yahoo and Pascal
    VOC 2008.
  • FaceTracer –
    15,000 faces annotated with 10 attributes and fiducial
    points.
  • PubFig –
    58,797 face images of 200 people with 73 attribute classifier
    outputs.
  • LFW –
    13,233 face images of 5,749 people with 73 attribute classifier
    outputs.
  • Human
    Attributes
     –
    8,000 people with annotated attributes. Check also
    this link for another
    dataset of human attributes.
  • SUN
    Attribute Database
     –
    Large-scale scene attribute database with a taxonomy of 102
    attributes.
  • ImageNet
    Attributes
     – Variety
    of attribute labels for the ImageNet dataset.
  • Relative
    attributes
     –
    Data for OSR and a subset of PubFig datasets. Check also
    this link for the
    WhittleSearch data.
  • Attribute
    Discovery
    Dataset
     –
    Images of shopping categories associated with textual
    descriptions.

Fine-grained
Visual Categorization 

Face
Detection 

  • FDDB –
    UMass face detection dataset and benchmark (5,000+ faces)
  • CMU/MIT –
    Classical face detection dataset.

Face
Recognition 

  • Face
    Recognition Homepage
     – Large
    collection of face recognition datasets.
  • LFW –
    UMass unconstrained face recognition dataset (13,000+ face
    images).
  • NIST
    Face Homepage
     – includes
    face recognition grand challenge (FRGC), vendor tests (FRVT) and
    others.
  • CMU
    Multi-PIE
     – contains more than 750,000
    images of 337 people, with 15 different views and 19 lighting
    conditions.
  • FERET –
    Classical face recognition dataset.
  • Deng
    Cai’s face dataset in Matlab
    Format
     –
    Easy to use if you want play with simple face datasets including
    Yale, ORL, PIE, and Extended Yale B.
  • SCFace –
    Low-resolution face dataset captured from surveillance
    cameras.

Handwritten
Digits 

  • MNIST –
    large dataset containing a training set of 60,000 examples, and a
    test set of 10,000 examples.

Pedestrian
Detection

Generic
Object Recognition 

  • ImageNet –
    Currently the largest visual recognition dataset in terms of number
    of categories and images.
  • Tiny
    Images
     – 80 million
    32×32 low resolution images.
  • Pascal
    VOC
     – One of the
    most influential visual recognition datasets.
  • Caltech
    101
     / Caltech
    256
     –
    Popular image datasets containing 101 and 256 object categories,
    respectively.
  • MIT
    LabelMe
     –
    Online annotation tool for building computer vision
    databases.

Scene
Recognition

Feature
Detection and Description 

Action
Recognition

RGBD
Recognition 

Reference:

[1]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html